Cancer-specific mortality in secondary bladder cancer after nephroureterectomy for upper tract urothelial carcinoma
Bibliographic record
Abstract
OBJECTIVE: To examine differences in cancer-specific mortality (CSM) in nonmetastatic upper tract urothelial carcinoma (UTUC) patients with vs. without secondary bladder cancer (BCa) after radical nephroureterectomy (RNU). METHODS: Within the Surveillance, Epidemiology, and End Results database (SEER 2000-2021), T1-T4N0M0 UTUC patients treated with RNU and diagnosed with secondary BCa were identified. A landmark approach was used, requiring the diagnosis of secondary BCa within 18 months of the UTUC diagnosis. Additionally, a minimum follow-up of 18 months after the UTUC diagnosis was required. Subsequently, Kaplan-Meier plots and time-dependent multivariable Cox regression (MCR) models were fitted. Sensitivity analyses were performed in patients with late BCa diagnoses (6 to 18 months after UTUC diagnosis). RESULTS: Of 3,013 eligible UTUC patients who fulfilled the landmark and follow-up criteria, 269 (9.0%) harbored secondary BCa. Ten-year CSM-free survival rates were respectively 60 vs 73% in patients with vs without secondary BCa. In MCR models, secondary BCa independently predicted higher CSM (hazard ratio [HR]: 1.53, p < 0.001). Subgroup analyses by tumor stage confirmed the independent predictor status of secondary BCa in T1-T2 stages (HR: 2.04, p < 0.001), primary renal pelvic (HR: 1.47, p = 0.003) and ureteral (HR: 1.63, p = 0.01) UTUC. Sensitivity analyses confirmed the independent predictor status of secondary BCa also in patients with late secondary BCa (HR: 1.68, p < 0.001). CONCLUSION: In general, secondary BCa in UTUC patients treated with RNU is associated with higher CSM. This disadvantage primarily affects patients with T1-T2 stage UTUC involving the ureter or renal pelvis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".